Data Mining Vs. Business intelligence: All you need to know
The surge in the use of mobile software and cloud services has sparked a new relationship between IT and business processes. This shift has made terminologies such as data mining, big data, and business intelligence the new buzz words for modern businesses. These terms are inter-related and often carry similar meaning which could confuse most […]READ MORE >>
The surge in the use of mobile software and cloud services has sparked a new relationship between IT and business processes. This shift has made terminologies such as data mining, big data, and business intelligence the new buzz words for modern businesses. These terms are inter-related and often carry similar meaning which could confuse most of us. Therefore, it is necessary to understand the key differences between the two important techniques widely used by businesses today – data mining and business intelligence.
Data mining is essentially the practice of examining large pre-existing databases with the motive to generate new information. Data mining specialists work with large datasets to identify insightful trends and patterns. In many cases, due to information overload data miners often overlook essential parameters that could make their companies excel. In short, data mining is all about deriving answers to issues you didn’t know you were looking for beforehand. Business intelligence, on the other hand, deals with the business processes and data analysis techniques which help to collate enterprise data. With the help of BI, companies can gain historical, current and predictive views of business operations. Data scientists devise Business intelligence tools to generate, aggregate, analyze, and visualize data, which in turn assists a company in better decision making.
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Data mining VS business intelligence
BI utilizes the past data in small or large scale. It is useful for the management to interpret past information and also to enhance their decision-making capabilities. On the contrary, data mining techniques utilize computational intelligence to discover relevant business factors on a small scale. The data helps management to identify potential opportunities and business factors that they were previously unaware of.
A prominent feature of business intelligence is that it is volumetric. These analytics tools are concerned with monitoring the predetermined key performance indicators(KPIs’). Data mining makes use of scientific methodology and algorithms to discover data patterns and behaviors. Furthermore, it helps identify management blind spots and provides an in-depth case-by-case statistical analysis.
Presentation of results
Business intelligence provides dashboards with consolidated views of the KPIs in the form of graphs and charts. In data mining, reports are presented with recommendations for strategic decision making.
Focus: BI helps to monitor factors such as price, value, temperature, total cost, etc. On the other hand, data mining identifies data patterns, which creates new analysis indicators for BI.
The volume of data
BI is exposed to large datasets; however, they are limited to the processing of relational databases. Data mining, however, deals with smaller datasets, accompanying higher data processing expenses.
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